Assessing the production and the impact of cultivated oysters in the Thau lagoon (Mediterranee, France) with a population dynamics model
Bibliographic record
Abstract
The Thau lagoon (France) has been studied for many years because of its ecological interest related to economic activities: shellfish cultivation, tourism, and industry. The standing stock of cultivated filter feeders is around 20 000 t and consists of two main species, the Japanese oyster Crassostrea gigas and the Mediterranean mussel Mytilus galloprovincialis. To predict changes in the standing stock and the annual production, a mathematical model of the population dynamics was defined. It was based on the continuous equation of the density as a function of the mortality rate, the individual growth rate, and the interindividual variability. The daily growth rate was derived from field surveys and depended on the phytoplankton concentration and individual weight. The model also took into account the rearing strategy of the producers defined by the timetable of seeding and harvesting, obtained by an inquiry among the producers and used to simulate real cases of standing stock changes. The model was also used to assess the potential impact on the environment through the computation of the food consumption, which was compared with the residence time and the primary production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".